Design of Experiments (DOE) is an important technique for root cause analysis (RCA) and process improvement. As an example, when potential trouble sources are identified from a cause and effect diagram, DOE can be used to determine which of the factors are likely to be important. DOE can also develop quantitative models of the nature y=f(x) (y is a function of x) where y is often a critical to quality characteristic.

While DOE is normally a subject for full-length college courses, the basics can be covered in a one-hour webinar. These fundamentals include hypothesis testing, which also carries over into acceptance sampling and statistical process control, as well as design of the experiment to exclude extraneous variation sources (randomization and blocking techniques).

Objective of the webinar:

Attendees will learn the fundamentals of DOE, some of which carry over into other industrial statistics applications such as acceptance sampling and statistical process control.

Hypothesis testing is the foundation of almost everything we do with industrial statistics.

The null hypothesis, or starting assumption, is that there is no difference between the experiment and the control, a production lot is acceptable, or a process is in control.

The alternate hypothesis is that the experiment differs from the control (is better than the control in an improvement activity), a production lot should be rejected, or a process is out of control and needs adjustment.

We must prove the alternate hypothesis beyond a quantitative reasonable doubt that is known as the Type I risk, alpha risk or, in acceptance sampling, the producer’s risk (of wrongly rejecting an acceptable lot).

DOE can save an enormous amount of time and money, as shown by comparison of an experiment performed in the late 19th century (prior to the development of industrial statistics) and an even more complex one performed roughly 100 years later. This underscores the value of DOE in the language of money, i.e. the language of upper management.

Understand the concepts of factors, levels, and interactions (the whole is greater or less than the sum of its parts). Factors such as machine or material are often identified from a cause and effect diagram during root cause analysis.

Recognize the need to exclude extraneous variation sources from the experiment through randomization and blocking, and also the need to use a sufficiently large sample to get meaningful results (replication).

Explain the results of an experiment in terms of its significance level or P value(chance that the observed results are due to random chance).

Areas Covered in the Session :

Value of DOE in the language of time and money, as shown by comparison of an experiment performed by Frederick Winslow Taylor during the late 19th century, and an even more complicated one performed by a pharmaceutical company that sought FDA approval for a diagnostic test

Hypothesis testing as the foundation of most industrial statistics applications including not only DOE but also statistical process control and acceptance sampling (e.g. ANSI/ASQ Z1.4 and ANSI/ASQ Z1.9)

Interactions, or situations in which the whole is greater or less than the sum of its parts. Interactions cannot be detected by one variable at a time experimentation.

This webinar will provide a sufficient foundation for attendees to work effectively with industrial statisticians, Six Sigma Green and Black Belts, and similar subject matter experts.

Who Should Attend:

Quality Departments

Manufacturing Departments

Engineering Departments

Technicians, Supervisors and Managers

FDB2494

William Levinson

William (Bill) A. Levinson, P.E., is the principal of Levinson Productivity Systems, P.C. He is an ASQ Fellow, Certified Quality Engineer, Quality Auditor, Quality Manager, Reliability Engineer, and Six Sigma Black Belt. He holds degrees in chemistry and chemical engineering from Pennsylvania State and Cornell Universities, and night school degrees in business administration and applied statistics from Union College, and he has given presentations at the ASQ World Conference, TOC World 2004, and other national conferences on productivity and quality.

Mr. Levinson is also the author of several books on quality, productivity, and management. Henry Ford’s Lean Vision is a comprehensive overview of the lean manufacturing and organizational management methods that Ford employed to achieve unprecedented bottom line results, and Beyond the Theory of Constraints describes how Ford’s elimination of variation from material transfer and processing times allowed him to come close to running a balanced factory at full capacity. Statistical Process Control for Real-World Applications shows what to do when the process doesn’t conform to the traditional bell curve assumption.

Refund Policy

Webinar Compliance reserves the right to cancel or reschedule any Webinar/event due to inevitable reasons such as insufficient registrations or circumstances beyond its control. All the attendees will be notified about the cancellation of the event, 24 hours prior to the start time of the Webinar event.

The cancelled Webinar, could be rescheduled and a New Date would be promptly intimated to the attendees.

In such an event, the attendee can opt for one of the below :

If the New Date is not of convenience, the webinar stream (1-Time Recording) may be availed.

The attendees may also opt to take a different webinar, which has a same price tag at a future date & time; they are welcome to do so.

On-Demand recordings (Past events) in exchange but equal to the original amount remitted.

A redeemable voucher (Valid for 12 months), which could be used to purchase any of our future events.

Webinar Compliance will process refund only if an event that has been cancelled, is not rescheduled within 90 days from the original scheduled date of the webinar.

If a webinar is canceled completely, an attendee may opt either of above points 2,3,4, or a full refund of the amount paid in a single settlement. The payment will be processed within 7 Business days from the day, we receive the refund request. However, Webinar Compliance will not be responsible for any penalties or other expenditure incurred due to the cancellation.

​Individual attendees can cancel their event for any specific reason. They must notify Webinar Compliance about the cancellation of their registration at least 48 hours prior to the event start date and time.

If the attendee fails to cancel the registration to the event within the above mentioned stipulated time or if fails to attend the event, no refund shall be made.

​

​For further clarification on the refund or cancellation policy, you can contact the support team over the phone or please write to us on support@webinarcompliance.com, with the transaction ID, event ID & event date in the subject column.